Dive into Deep Learning
원문 초록
This open-source book represents our attempt to make deep learning approachable, teaching readers the concepts, the context, and the code. The entire book is drafted in Jupyter notebooks, seamlessly integrating exposition figures, math, and interactive examples with self-contained code. Our goal is to offer a resource that could (i) be freely available for everyone; (ii) offer sufficient technical depth to provide a starting point on the path to actually becoming an applied machine learning scientist; (iii) include runnable code, showing readers how to solve problems in practice; (iv) allow for rapid updates, both by us and also by the community at large; (v) be complemented by a forum for interactive discussion of technical details and to answer questions.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Personalized Exercise Recommendation with Semantically-Grounded Knowledge Tracing
Reasoning or Memorization? Unreliable Results of Reinforcement Learning Due to Data Contamination
A Practical Two-Stage Recipe for Mathematical LLMs: Maximizing Accuracy with SFT and Efficiency with Reinforcement Learning
The Delta Learning Hypothesis: Preference Tuning on Weak Data can Yield Strong Gains
LLMThinkBench: Towards Basic Math Reasoning and Overthinking in Large Language Models
EvoAgentX: An Automated Framework for Evolving Agentic Workflows
토론
이 논문과 연결된 포럼 글이 아직 없습니다.